Review: The impact of climate change awareness on children's mental well‐being and negative emotions – a scoping review
Bibliographic record
Abstract
BACKGROUND: Climate change is a threat to children's physical health, but there are also implications for mental well-being. Additionally, children may experience negative emotional responses stemming from an overarching awareness of the imminent threats to the planet due to climate change. METHOD: Using a scoping review, we examined the impact of climate change awareness on children's mental well-being and negative emotions. Our aim was to identify and describe the existing literature and highlight priorities for future research. Three specific objectives guided the review: (1) to identify and provide an overview of research regarding the impact of climate change awareness on children's mental well-being and negative emotions; (2) to summarize and clarify the terminology related to climate change awareness and children's mental well-being and negative emotions; and (3) to make recommendations for areas of future research. RESULTS: Thirty-three articles were included in a narrative synthesis. Many articles were reviews or editorials/commentaries. Of the empirical research, most were from Europe, North America, and Australia. The articles emphasized a large range of negative emotions that children felt about climate change, with anxiety and worry being the most researched and discussed. CONCLUSIONS: The research on the impact of awareness of climate change on children's mental well-being and negative emotions is in its early phases. Efforts are needed to advance conceptual clarity and operationalize concepts. Additionally, there is a need for research into the impact of climate change awareness on children's mental well-being and negative emotions among a greater diversity of people and places. Existing studies provide an encouraging basis from which to develop future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".